Update app.py

app.py

@@ -2,29 +2,27 @@ import os
 import yfinance as yf
 import pandas as pd
 import numpy as np
+import requests
 import plotly.graph_objects as go
 import streamlit as st
-import requests
 from datetime import timedelta
 from scipy.stats import norm
 
+# Load API key from environment variables
+FMP_API_KEY = os.getenv("FMP_API_KEY")
+
 # Define functions
 def fetch_earnings_data(ticker, limit=99):
     """
-    Fetch earnings data using the FMP API.
+    Fetch earnings data from the Financial Modeling Prep API.
     """
     try:
-        # Load API key from environment
-        api_key = os.getenv("FMP_API_KEY")
-        if not api_key:
-            raise ValueError("FMP API key is not set in the environment variables.")
-        
-        url = f"https://financialmodelingprep.com/api/v3/earnings-surprises/{ticker}?apikey={api_key}"
+        url = f"https://financialmodelingprep.com/api/v3/earnings-surprises/{ticker}?apikey={FMP_API_KEY}"
         response = requests.get(url)
         response.raise_for_status()
         data = response.json()
-
-        # Parse and format data
+        
+        # Parse the API response into a DataFrame
         earnings_data = pd.DataFrame(data)
         earnings_data['date'] = pd.to_datetime(earnings_data['date'])
         earnings_data.set_index('date', inplace=True)
@@ -40,15 +38,14 @@ def fetch_earnings_data(ticker, limit=99):
             (earnings_data['Actual EPS'] - earnings_data['EPS Estimate'])
             / earnings_data['EPS Estimate']
         ) * 100
-
         return earnings_data.head(limit)
     except Exception as e:
         st.warning(f"There was an issue fetching earnings data: {e}")
-        return pd.DataFrame()
+        return pd.DataFrame()  # Return an empty DataFrame on failure
 
 def fetch_stock_data(ticker, start_date, end_date, buffer_days):
     """
-    Fetch historical stock data from Yahoo Finance.
+    Fetch historical stock data using yfinance.
     """
     try:
         start_date = start_date - pd.Timedelta(days=buffer_days)
@@ -57,80 +54,86 @@ def fetch_stock_data(ticker, start_date, end_date, buffer_days):
         stock_data.index = stock_data.index.tz_localize(None)
         return stock_data
     except Exception as e:
-        st.warning("There was an issue fetching stock data. Please try again later.")
+        st.warning(f"There was an issue fetching stock data: {e}")
         return pd.DataFrame()
 
 def calculate_metrics(stock_data):
     """
-    Calculate additional stock metrics such as returns and 20-day rolling volatility.
+    Add metrics like daily returns and rolling volatility to the stock data.
     """
     if not stock_data.empty:
         stock_data['Returns'] = stock_data['Close'].pct_change()
         stock_data['20D Volatility'] = stock_data['Returns'].rolling(window=20).std()
     return stock_data
 
-# Remaining functions are identical to the original ones provided in your code.
-# They include plot_stock_price_with_earnings, ensure_window_size, plot_normalized_price_movements,
-# plot_volatility_around_earnings, plot_volume_around_earnings, compute_price_effect, plot_price_effects,
-# plot_surprise_vs_price_effect, monte_carlo_simulation, etc.
-
-# Streamlit app
-st.set_page_config(layout="wide")
-st.title("Earnings Announcements Analysis")
-st.write(
+def plot_stock_price_with_earnings(stock_data, earnings_dates, ticker):
     """
-    This tool helps you analyze the impact of earnings announcements 
-    on a company's stock price. By providing a ticker symbol and configuring the analysis parameters in the sidebar, 
-    you can explore various aspects of stock price behavior around earnings dates and the likelihood of future movements.
+    Plot stock prices with earnings surprises.
     """
-)
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=stock_data.index, y=stock_data['Close'], mode='lines', name='Stock Price'))
+    
+    for index, row in earnings_dates.iterrows():
+        date = index
+        if date not in stock_data.index:
+            date = stock_data.index[stock_data.index.get_indexer([date], method='nearest')[0]]
+        surprise = row['Surprise(%)']
+        color = 'green' if surprise > 0 else 'red'
+        marker = '^' if surprise > 0 else 'v'
+        fig.add_trace(go.Scatter(
+            x=[date], y=[stock_data.loc[date, 'Close']],
+            mode='markers',
+            marker=dict(symbol='triangle-up' if marker == '^' else 'triangle-down', size=12, color=color),
+            name=f"{'Positive' if surprise > 0 else 'Negative'} EPS Surprise"
+        ))
 
-# Sidebar inputs
-st.sidebar.title("Input Parameters")
-with st.sidebar.expander("How to Use", expanded=False):
-    st.write("""
-    **How to use this app:**
-    1. Set the `FMP_API_KEY` environment variable with your Financial Modeling Prep API key.
-    2. Enter the ticker symbol for the stock you want to analyze.
-    3. Adjust the pre and post-announcement windows to define the period around earnings dates.
-    4. Set the threshold percentage for price movement analysis.
-    5. Configure buffer days for fetching stock data.
-    6. Enter the implied volatility and days until earnings for Monte Carlo simulation.
-    7. Click the "Run Analysis" button to start the analysis.
-    """)
+    fig.update_layout(
+        title=f'{ticker} Stock Price with Earnings Surprises',
+        xaxis_title='Date', yaxis_title='Price',
+        template='plotly_white'
+    )
+    return fig
 
-# Ticker and date inputs
-with st.sidebar.expander("Ticker and Date Selection", expanded=True):
-    ticker = st.text_input("Enter Ticker Symbol", "MSFT", help="Enter the ticker symbol of the stock.")
-    pre_announcement_window = st.number_input("Pre-announcement Window (days)", value=5, min_value=1, help="Days before the earnings announcement.")
-    post_announcement_window = st.number_input("Post-announcement Window (days)", value=10, min_value=1, help="Days after the earnings announcement.")
+# Streamlit app setup
+st.set_page_config(layout="wide")
+st.title("Earnings Analysis with Financial Modeling Prep API")
 
-# Analysis Parameters
-with st.sidebar.expander("Analysis Parameters", expanded=True):
-    threshold_percentage = st.number_input("Threshold Percentage", value=0.10, min_value=0.01, max_value=1.0, step=0.01, help="Threshold for price change analysis.")
-    buffer_days = st.number_input("Buffer Days", value=10, min_value=1, help="Number of buffer days around the earnings dates.")
-    implied_volatility = st.number_input("Implied Volatility", value=0.30, min_value=0.01, max_value=1.0, step=0.01, help="Implied volatility for Monte Carlo simulation.")
-    days_until_earnings = st.number_input("Days Until Earnings", value=10, min_value=1, help="Days until the earnings announcement.")
-    num_simulations = st.number_input("Number of Simulations for Monte Carlo", value=10000, min_value=100, help="Number of simulations for Monte Carlo analysis.")
+# Sidebar inputs
+st.sidebar.title("Input Parameters")
+ticker = st.sidebar.text_input("Enter Ticker Symbol", "AAPL", help="Enter the stock ticker symbol.")
+pre_announcement_window = st.sidebar.number_input("Pre-announcement Window (days)", value=5, min_value=1)
+post_announcement_window = st.sidebar.number_input("Post-announcement Window (days)", value=5, min_value=1)
+buffer_days = st.sidebar.number_input("Buffer Days", value=10, min_value=1)
 
-# Run Analysis
 if st.sidebar.button("Run Analysis"):
-    try:
-        # Fetch earnings data
-        earnings_dates = fetch_earnings_data(ticker)
-        if earnings_dates.empty:
-            st.error("Failed to fetch earnings data. Please check the ticker and API key.")
+    if not FMP_API_KEY:
+        st.error("Please set your FMP_API_KEY in the environment variables.")
+    else:
+        # Fetch earnings and stock data
+        earnings_data = fetch_earnings_data(ticker)
+        if earnings_data.empty:
+            st.error("Failed to fetch earnings data. Please check the ticker or API key.")
         else:
-            # Fetch stock data
-            start_date = earnings_dates.index.min()
-            end_date = earnings_dates.index.max()
-            stock_data = fetch_stock_data(ticker, start_date, end_date, buffer_days)
-
+            st.subheader(f"Earnings Data for {ticker}")
+            st.dataframe(earnings_data)
+            
+            min_date, max_date = earnings_data.index.min(), earnings_data.index.max()
+            stock_data = fetch_stock_data(ticker, min_date, max_date, buffer_days)
             if stock_data.empty:
-                st.error("Failed to fetch stock data. Please try again later.")
+                st.error("Failed to fetch stock data.")
             else:
+                # Calculate additional metrics
                 stock_data = calculate_metrics(stock_data)
-                st.write("Earnings data and stock data successfully fetched!")
-                # Additional plotting and analysis here
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
+
+                # Plot stock price with earnings
+                st.subheader(f"{ticker} Stock Price with Earnings Surprises")
+                st.plotly_chart(plot_stock_price_with_earnings(stock_data, earnings_data, ticker), use_container_width=True)
+
+# Hide Streamlit UI elements
+hide_streamlit_style = """
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    </style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)